Overview

Brought to you by YData

Dataset statistics

Number of variables11
Number of observations247903
Missing cells13591
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory34.2 MiB
Average record size in memory144.5 B

Variable types

Numeric10
Categorical1

Alerts

Pollen_analysis is highly overall correlated with PriceHigh correlation
Price is highly overall correlated with Pollen_analysisHigh correlation

Reproduction

Analysis started2024-11-20 09:48:12.818245
Analysis finished2024-11-20 09:48:56.079745
Duration43.26 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

CS
Real number (ℝ)

Distinct901
Distinct (%)0.4%
Missing1388
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean5.4995229
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-11-20T09:48:56.312118image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.45
Q13.26
median5.5
Q37.74
95-th percentile9.55
Maximum10
Range9
Interquartile range (IQR)4.48

Descriptive statistics

Standard deviation2.5939109
Coefficient of variation (CV)0.47166107
Kurtosis-1.1964186
Mean5.4995229
Median Absolute Deviation (MAD)2.24
Skewness0.00034491835
Sum1355714.9
Variance6.7283738
MonotonicityNot monotonic
2024-11-20T09:48:56.770447image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.84 329
 
0.1%
2.81 323
 
0.1%
4.61 320
 
0.1%
4.58 320
 
0.1%
3.68 319
 
0.1%
9.25 316
 
0.1%
8.38 315
 
0.1%
8.6 314
 
0.1%
3.02 313
 
0.1%
7.02 312
 
0.1%
Other values (891) 243334
98.2%
(Missing) 1388
 
0.6%
ValueCountFrequency (%)
1 141
0.1%
1.01 225
0.1%
1.02 258
0.1%
1.03 274
0.1%
1.04 250
0.1%
1.05 277
0.1%
1.06 267
0.1%
1.07 297
0.1%
1.08 284
0.1%
1.09 263
0.1%
ValueCountFrequency (%)
10 126
0.1%
9.99 255
0.1%
9.98 272
0.1%
9.97 255
0.1%
9.96 281
0.1%
9.95 257
0.1%
9.94 281
0.1%
9.93 275
0.1%
9.92 275
0.1%
9.91 258
0.1%

Density
Real number (ℝ)

Distinct66
Distinct (%)< 0.1%
Missing1363
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean1.5355293
Minimum1.21
Maximum1.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-11-20T09:48:57.203343image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.21
5-th percentile1.24
Q11.37
median1.54
Q31.7
95-th percentile1.83
Maximum1.86
Range0.65
Interquartile range (IQR)0.33

Descriptive statistics

Standard deviation0.18781733
Coefficient of variation (CV)0.12231439
Kurtosis-1.2004232
Mean1.5355293
Median Absolute Deviation (MAD)0.16
Skewness-0.00083301322
Sum378569.4
Variance0.035275351
MonotonicityNot monotonic
2024-11-20T09:48:57.670321image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.84 3912
 
1.6%
1.35 3909
 
1.6%
1.29 3902
 
1.6%
1.49 3881
 
1.6%
1.76 3879
 
1.6%
1.53 3876
 
1.6%
1.78 3872
 
1.6%
1.77 3870
 
1.6%
1.38 3854
 
1.6%
1.62 3846
 
1.6%
Other values (56) 207739
83.8%
ValueCountFrequency (%)
1.21 1893
0.8%
1.22 3770
1.5%
1.23 3781
1.5%
1.24 3702
1.5%
1.25 3765
1.5%
1.26 3828
1.5%
1.27 3668
1.5%
1.28 3772
1.5%
1.29 3902
1.6%
1.3 3837
1.5%
ValueCountFrequency (%)
1.86 1963
0.8%
1.85 3787
1.5%
1.84 3912
1.6%
1.83 3756
1.5%
1.82 3818
1.5%
1.81 3768
1.5%
1.8 3829
1.5%
1.79 3803
1.5%
1.78 3872
1.6%
1.77 3870
1.6%

WC
Real number (ℝ)

Distinct1301
Distinct (%)0.5%
Missing1351
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean18.502084
Minimum12
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-11-20T09:48:58.124784image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile12.65
Q115.26
median18.51
Q321.75
95-th percentile24.34
Maximum25
Range13
Interquartile range (IQR)6.49

Descriptive statistics

Standard deviation3.7485739
Coefficient of variation (CV)0.20260279
Kurtosis-1.1994719
Mean18.502084
Median Absolute Deviation (MAD)3.25
Skewness-0.0025122762
Sum4561725.8
Variance14.051806
MonotonicityNot monotonic
2024-11-20T09:48:58.605144image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
14.35 237
 
0.1%
13.69 232
 
0.1%
20.81 232
 
0.1%
18.83 230
 
0.1%
20.39 230
 
0.1%
17.47 228
 
0.1%
20.09 226
 
0.1%
23.73 225
 
0.1%
12.84 225
 
0.1%
23.44 225
 
0.1%
Other values (1291) 244262
98.5%
(Missing) 1351
 
0.5%
ValueCountFrequency (%)
12 111
< 0.1%
12.01 184
0.1%
12.02 192
0.1%
12.03 195
0.1%
12.04 185
0.1%
12.05 219
0.1%
12.06 205
0.1%
12.07 177
0.1%
12.08 183
0.1%
12.09 186
0.1%
ValueCountFrequency (%)
25 114
< 0.1%
24.99 185
0.1%
24.98 196
0.1%
24.97 174
0.1%
24.96 181
0.1%
24.95 186
0.1%
24.94 207
0.1%
24.93 200
0.1%
24.92 173
0.1%
24.91 204
0.1%

pH
Real number (ℝ)

Distinct501
Distinct (%)0.2%
Missing1364
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean4.9958927
Minimum2.5
Maximum7.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-11-20T09:48:59.043017image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile2.75
Q13.74
median4.99
Q36.25
95-th percentile7.25
Maximum7.5
Range5
Interquartile range (IQR)2.51

Descriptive statistics

Standard deviation1.4442264
Coefficient of variation (CV)0.28908274
Kurtosis-1.2015603
Mean4.9958927
Median Absolute Deviation (MAD)1.25
Skewness0.0029723348
Sum1231682.4
Variance2.0857898
MonotonicityNot monotonic
2024-11-20T09:48:59.493068image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.89 572
 
0.2%
6.62 571
 
0.2%
6.85 563
 
0.2%
6.83 555
 
0.2%
5.18 552
 
0.2%
2.81 551
 
0.2%
2.72 549
 
0.2%
7.47 548
 
0.2%
7.45 544
 
0.2%
3.23 544
 
0.2%
Other values (491) 240990
97.2%
(Missing) 1364
 
0.6%
ValueCountFrequency (%)
2.5 251
0.1%
2.51 519
0.2%
2.52 499
0.2%
2.53 497
0.2%
2.54 496
0.2%
2.55 524
0.2%
2.56 484
0.2%
2.57 495
0.2%
2.58 460
0.2%
2.59 465
0.2%
ValueCountFrequency (%)
7.5 250
0.1%
7.49 477
0.2%
7.48 513
0.2%
7.47 548
0.2%
7.46 481
0.2%
7.45 544
0.2%
7.44 503
0.2%
7.43 448
0.2%
7.42 479
0.2%
7.41 503
0.2%

EC
Real number (ℝ)

Distinct21
Distinct (%)< 0.1%
Missing1317
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean0.79996971
Minimum0.7
Maximum0.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-11-20T09:48:59.914120image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.7
5-th percentile0.71
Q10.75
median0.8
Q30.85
95-th percentile0.89
Maximum0.9
Range0.2
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.057914835
Coefficient of variation (CV)0.072396285
Kurtosis-1.1875249
Mean0.79996971
Median Absolute Deviation (MAD)0.05
Skewness0.0007872853
Sum197261.33
Variance0.0033541281
MonotonicityNot monotonic
2024-11-20T09:49:00.360172image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.78 12488
 
5.0%
0.77 12452
 
5.0%
0.72 12430
 
5.0%
0.86 12394
 
5.0%
0.74 12390
 
5.0%
0.87 12362
 
5.0%
0.82 12355
 
5.0%
0.89 12345
 
5.0%
0.75 12343
 
5.0%
0.8 12343
 
5.0%
Other values (11) 122684
49.5%
ValueCountFrequency (%)
0.7 6319
2.5%
0.71 12206
4.9%
0.72 12430
5.0%
0.73 12210
4.9%
0.74 12390
5.0%
0.75 12343
5.0%
0.76 12136
4.9%
0.77 12452
5.0%
0.78 12488
5.0%
0.79 12248
4.9%
ValueCountFrequency (%)
0.9 6228
2.5%
0.89 12345
5.0%
0.88 12247
4.9%
0.87 12362
5.0%
0.86 12394
5.0%
0.85 12302
5.0%
0.84 12205
4.9%
0.83 12252
4.9%
0.82 12355
5.0%
0.81 12331
5.0%

F
Real number (ℝ)

Distinct3001
Distinct (%)1.2%
Missing1372
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean34.970109
Minimum20
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-11-20T09:49:00.802184image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21.48
Q127.46
median34.97
Q342.47
95-th percentile48.48
Maximum50
Range30
Interquartile range (IQR)15.01

Descriptive statistics

Standard deviation8.6562706
Coefficient of variation (CV)0.24753342
Kurtosis-1.1990631
Mean34.970109
Median Absolute Deviation (MAD)7.5
Skewness0.00090201583
Sum8621216
Variance74.931021
MonotonicityNot monotonic
2024-11-20T09:49:01.126386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.98 115
 
< 0.1%
20.33 112
 
< 0.1%
46.66 112
 
< 0.1%
27.61 111
 
< 0.1%
31.37 111
 
< 0.1%
31.9 111
 
< 0.1%
48.76 110
 
< 0.1%
36.2 109
 
< 0.1%
42.15 109
 
< 0.1%
26.51 109
 
< 0.1%
Other values (2991) 245422
99.0%
(Missing) 1372
 
0.6%
ValueCountFrequency (%)
20 37
< 0.1%
20.01 82
< 0.1%
20.02 87
< 0.1%
20.03 80
< 0.1%
20.04 77
< 0.1%
20.05 86
< 0.1%
20.06 82
< 0.1%
20.07 74
< 0.1%
20.08 82
< 0.1%
20.09 75
< 0.1%
ValueCountFrequency (%)
50 44
< 0.1%
49.99 103
< 0.1%
49.98 82
< 0.1%
49.97 81
< 0.1%
49.96 77
< 0.1%
49.95 88
< 0.1%
49.94 77
< 0.1%
49.93 87
< 0.1%
49.92 77
< 0.1%
49.91 73
< 0.1%

G
Real number (ℝ)

Distinct2501
Distinct (%)1.0%
Missing1357
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean32.50228
Minimum20
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-11-20T09:49:01.360806image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21.24
Q126.23
median32.5
Q338.76
95-th percentile43.75
Maximum45
Range25
Interquartile range (IQR)12.53

Descriptive statistics

Standard deviation7.2266721
Coefficient of variation (CV)0.22234354
Kurtosis-1.2023656
Mean32.50228
Median Absolute Deviation (MAD)6.26
Skewness-0.00058428499
Sum8013307.2
Variance52.22479
MonotonicityNot monotonic
2024-11-20T09:49:01.592781image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.71 131
 
0.1%
43.52 131
 
0.1%
39.45 130
 
0.1%
37.59 130
 
0.1%
21.89 128
 
0.1%
33.74 128
 
0.1%
22.71 127
 
0.1%
20.46 126
 
0.1%
33.01 126
 
0.1%
43.28 126
 
0.1%
Other values (2491) 245263
98.9%
(Missing) 1357
 
0.5%
ValueCountFrequency (%)
20 42
 
< 0.1%
20.01 105
< 0.1%
20.02 105
< 0.1%
20.03 103
< 0.1%
20.04 92
< 0.1%
20.05 97
< 0.1%
20.06 87
< 0.1%
20.07 89
< 0.1%
20.08 111
< 0.1%
20.09 99
< 0.1%
ValueCountFrequency (%)
45 49
< 0.1%
44.99 114
< 0.1%
44.98 94
< 0.1%
44.97 103
< 0.1%
44.96 99
< 0.1%
44.95 110
< 0.1%
44.94 102
< 0.1%
44.93 94
< 0.1%
44.92 114
< 0.1%
44.91 100
< 0.1%

Pollen_analysis
Categorical

High correlation 

Distinct19
Distinct (%)< 0.1%
Missing1351
Missing (%)0.5%
Memory size15.3 MiB
Eucalyptus
 
13126
Heather
 
13125
Avocado
 
13123
Thyme
 
13082
Sunflower
 
13073
Other values (14)
181023 

Length

Max length14
Median length9
Mean length7.6328604
Min length4

Characters and Unicode

Total characters1881897
Distinct characters35
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBlueberry
2nd rowAlfalfa
3rd rowChestnut
4th rowBlueberry
5th rowAlfalfa

Common Values

ValueCountFrequency (%)
Eucalyptus 13126
 
5.3%
Heather 13125
 
5.3%
Avocado 13123
 
5.3%
Thyme 13082
 
5.3%
Sunflower 13073
 
5.3%
Sage 13047
 
5.3%
Lavender 13029
 
5.3%
Blueberry 13021
 
5.3%
Alfalfa 12983
 
5.2%
Buckwheat 12960
 
5.2%
Other values (9) 115983
46.8%

Length

2024-11-20T09:49:01.809341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
eucalyptus 13126
 
5.1%
heather 13125
 
5.1%
avocado 13123
 
5.1%
thyme 13082
 
5.0%
sunflower 13073
 
5.0%
sage 13047
 
5.0%
lavender 13029
 
5.0%
blueberry 13021
 
5.0%
alfalfa 12983
 
5.0%
buckwheat 12960
 
5.0%
Other values (10) 128919
49.7%

Most occurring characters

ValueCountFrequency (%)
e 220666
 
11.7%
a 194764
 
10.3%
r 129774
 
6.9%
l 129550
 
6.9%
o 129475
 
6.9%
u 103878
 
5.5%
t 65079
 
3.5%
c 65021
 
3.5%
n 64895
 
3.4%
s 64781
 
3.4%
Other values (25) 714014
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1609473
85.5%
Uppercase Letter 259488
 
13.8%
Space Separator 12936
 
0.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 220666
13.7%
a 194764
12.1%
r 129774
 
8.1%
l 129550
 
8.0%
o 129475
 
8.0%
u 103878
 
6.5%
t 65079
 
4.0%
c 65021
 
4.0%
n 64895
 
4.0%
s 64781
 
4.0%
Other values (12) 441590
27.4%
Uppercase Letter
ValueCountFrequency (%)
B 51862
20.0%
A 39012
15.0%
S 26120
10.1%
T 25797
9.9%
C 25771
9.9%
E 13126
 
5.1%
H 13125
 
5.1%
L 13029
 
5.0%
W 12938
 
5.0%
O 12936
 
5.0%
Other values (2) 25772
9.9%
Space Separator
ValueCountFrequency (%)
12936
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1868961
99.3%
Common 12936
 
0.7%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 220666
 
11.8%
a 194764
 
10.4%
r 129774
 
6.9%
l 129550
 
6.9%
o 129475
 
6.9%
u 103878
 
5.6%
t 65079
 
3.5%
c 65021
 
3.5%
n 64895
 
3.5%
s 64781
 
3.5%
Other values (24) 701078
37.5%
Common
ValueCountFrequency (%)
12936
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1881897
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 220666
 
11.7%
a 194764
 
10.3%
r 129774
 
6.9%
l 129550
 
6.9%
o 129475
 
6.9%
u 103878
 
5.5%
t 65079
 
3.5%
c 65021
 
3.5%
n 64895
 
3.4%
s 64781
 
3.4%
Other values (25) 714014
37.9%

Viscosity
Real number (ℝ)

Distinct214079
Distinct (%)86.8%
Missing1329
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean5752.3262
Minimum1500.05
Maximum9999.97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-11-20T09:49:02.024038image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1500.05
5-th percentile1920.708
Q13627.175
median5752.66
Q37886.04
95-th percentile9572.8715
Maximum9999.97
Range8499.92
Interquartile range (IQR)4258.865

Descriptive statistics

Standard deviation2455.7221
Coefficient of variation (CV)0.42690939
Kurtosis-1.2018505
Mean5752.3262
Median Absolute Deviation (MAD)2129.435
Skewness-0.0035178119
Sum1.4183741 × 109
Variance6030570.8
MonotonicityNot monotonic
2024-11-20T09:49:02.246775image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1670.29 6
 
< 0.1%
6000.5 5
 
< 0.1%
7772.09 5
 
< 0.1%
9627.46 5
 
< 0.1%
8663.64 5
 
< 0.1%
6301.34 5
 
< 0.1%
3376.6 5
 
< 0.1%
4198.6 5
 
< 0.1%
6383.17 5
 
< 0.1%
6205.04 5
 
< 0.1%
Other values (214069) 246523
99.4%
(Missing) 1329
 
0.5%
ValueCountFrequency (%)
1500.05 1
< 0.1%
1500.06 1
< 0.1%
1500.23 1
< 0.1%
1500.24 1
< 0.1%
1500.28 1
< 0.1%
1500.3 1
< 0.1%
1500.33 2
< 0.1%
1500.35 1
< 0.1%
1500.43 1
< 0.1%
1500.45 1
< 0.1%
ValueCountFrequency (%)
9999.97 1
 
< 0.1%
9999.91 1
 
< 0.1%
9999.85 1
 
< 0.1%
9999.83 1
 
< 0.1%
9999.77 1
 
< 0.1%
9999.76 3
< 0.1%
9999.74 1
 
< 0.1%
9999.73 1
 
< 0.1%
9999.68 1
 
< 0.1%
9999.58 1
 
< 0.1%

Purity
Real number (ℝ)

Distinct24
Distinct (%)< 0.1%
Missing1399
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean0.82445717
Minimum0.61
Maximum1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-11-20T09:49:02.446437image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0.61
5-th percentile0.64
Q10.66
median0.82
Q30.97
95-th percentile1
Maximum1
Range0.39
Interquartile range (IQR)0.31

Descriptive statistics

Standard deviation0.13940729
Coefficient of variation (CV)0.16908979
Kurtosis-1.5211469
Mean0.82445717
Median Absolute Deviation (MAD)0.16
Skewness-0.071665444
Sum203231.99
Variance0.019434391
MonotonicityNot monotonic
2024-11-20T09:49:02.999407image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 52543
21.2%
0.66 40028
16.1%
0.82 26901
10.9%
0.64 24231
9.8%
0.8 16455
 
6.6%
0.68 13755
 
5.5%
0.97 13345
 
5.4%
0.84 10069
 
4.1%
0.95 6868
 
2.8%
0.9 6301
 
2.5%
Other values (14) 36008
14.5%
ValueCountFrequency (%)
0.61 3944
 
1.6%
0.63 2014
 
0.8%
0.64 24231
9.8%
0.66 40028
16.1%
0.68 13755
 
5.5%
0.77 2683
 
1.1%
0.79 1366
 
0.6%
0.8 16455
6.6%
0.82 26901
10.9%
0.84 10069
 
4.1%
ValueCountFrequency (%)
1 52543
21.2%
0.99 5136
 
2.1%
0.98 286
 
0.1%
0.97 13345
 
5.4%
0.96 1143
 
0.5%
0.95 6868
 
2.8%
0.94 2908
 
1.2%
0.92 3617
 
1.5%
0.9 6301
 
2.5%
0.89 1605
 
0.6%

Price
Real number (ℝ)

High correlation 

Distinct708
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean594.80764
Minimum128.72
Maximum976.69
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 MiB
2024-11-20T09:49:03.233899image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum128.72
5-th percentile185.13
Q1433
median612.96
Q3770.22
95-th percentile946.46
Maximum976.69
Range847.97
Interquartile range (IQR)337.22

Descriptive statistics

Standard deviation233.62797
Coefficient of variation (CV)0.39277903
Kurtosis-0.87617561
Mean594.80764
Median Absolute Deviation (MAD)166.37
Skewness-0.2447019
Sum1.474546 × 108
Variance54582.029
MonotonicityNot monotonic
2024-11-20T09:49:03.459221image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
926.3 5591
 
2.3%
976.69 5543
 
2.2%
612.96 2969
 
1.2%
646.3 2935
 
1.2%
885.99 2912
 
1.2%
694.53 2873
 
1.2%
603.85 2842
 
1.1%
251.38 2810
 
1.1%
684.45 2805
 
1.1%
916.22 2798
 
1.1%
Other values (698) 213825
86.3%
ValueCountFrequency (%)
128.72 207
 
0.1%
132.95 104
 
< 0.1%
134 308
 
0.1%
134.43 991
0.4%
138.23 167
 
0.1%
138.65 489
 
0.2%
139.71 1461
0.6%
143.93 705
0.3%
153.27 218
 
0.1%
158.3 108
 
< 0.1%
ValueCountFrequency (%)
976.69 5543
2.2%
968.87 596
 
0.2%
966.61 2787
1.1%
958.87 262
 
0.1%
957.14 33
 
< 0.1%
949.32 1080
 
0.4%
947.26 17
 
< 0.1%
946.46 2748
1.1%
944.43 354
 
0.1%
942.48 120
 
< 0.1%

Interactions

2024-11-20T09:48:50.753503image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:26.085426image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:29.275069image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:31.691470image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:34.782080image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:37.796274image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:41.179341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:43.347453image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:45.112858image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:47.591691image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:51.048064image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:26.384052image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:29.574160image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:31.990766image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:35.075645image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:38.315863image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:41.474921image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:43.515629image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:45.279448image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:47.899817image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:51.358952image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:26.689066image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:29.892401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:32.305825image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:35.385085image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:38.635202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:41.783346image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:43.694021image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-11-20T09:48:48.499344image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:51.661045image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:26.994130image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:30.070998image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:32.617148image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:35.687433image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:38.950588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:42.089434image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:43.867332image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:45.631900image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:48.843024image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:51.958790image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:27.284381image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:30.245231image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:32.915140image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:35.979123image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:39.259429image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:42.260401image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:44.041855image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:45.798768image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:49.157734image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:52.276820image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:27.602282image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:30.439025image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:33.240714image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:36.295972image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:39.586088image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:42.453108image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:44.230974image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:46.030135image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:49.493007image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:52.582515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:27.905202image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:30.619142image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:33.549563image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:36.600814image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:39.923633image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:42.627405image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:44.403936image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:46.363970image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:49.836708image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:52.893557image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:28.368769image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:30.794682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-11-20T09:48:44.578164image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
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2024-11-20T09:48:40.539200image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:42.974297image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:44.740873image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:46.967329image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:50.216082image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:53.522288image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:28.981948image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:31.392758image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:34.483435image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:37.504565image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:40.874179image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:43.174797image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:44.940233image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:47.296181image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2024-11-20T09:48:50.442454image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2024-11-20T09:49:03.616428image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
CSDensityECFGPollen_analysisPricePurityViscosityWCpH
CS1.000-0.0010.000-0.0030.0010.0000.0370.078-0.0000.0010.000
Density-0.0011.000-0.0040.0010.0030.002-0.053-0.115-0.003-0.0000.002
EC0.000-0.0041.000-0.002-0.0020.000-0.0000.001-0.0020.001-0.001
F-0.0030.001-0.0021.000-0.0010.000-0.002-0.0010.0000.0010.000
G0.0010.003-0.002-0.0011.0000.004-0.0040.0000.0010.003-0.000
Pollen_analysis0.0000.0020.0000.0000.0041.0000.5100.0000.0020.0000.000
Price0.037-0.053-0.000-0.002-0.0040.5101.0000.4520.011-0.015-0.108
Purity0.078-0.1150.001-0.0010.0000.0000.4521.0000.022-0.031-0.231
Viscosity-0.000-0.003-0.0020.0000.0010.0020.0110.0221.000-0.001-0.003
WC0.001-0.0000.0010.0010.0030.000-0.015-0.031-0.0011.0000.001
pH0.0000.002-0.0010.000-0.0000.000-0.108-0.231-0.0030.0011.000

Missing values

2024-11-20T09:48:53.941323image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-20T09:48:54.681969image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-11-20T09:48:55.698562image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CSDensityWCpHECFGPollen_analysisViscosityPurityPrice
02.811.7523.046.290.7639.0233.63Blueberry4844.500.68645.24
19.471.8217.507.200.7138.1534.41Alfalfa6689.020.89385.85
24.611.8423.727.310.8027.4734.36Chestnut6883.600.66639.64
31.771.4016.614.010.7831.5228.15Blueberry7167.561.00946.46
46.111.2519.634.820.9029.6542.52Alfalfa5125.441.00432.62
52.171.3520.674.110.7527.1943.54Borage3967.300.80593.55
67.151.5713.375.790.8943.3844.94Sunflower7384.930.95838.98
73.171.2323.356.640.8543.5036.33Chestnut5598.870.66639.64
84.981.7316.606.610.7422.4944.74Orange Blossom8800.100.95238.05
98.491.5015.754.500.8649.5938.13Blueberry2675.600.82777.84
CSDensityWCpHECFGPollen_analysisViscosityPurityPrice
2478933.241.4717.166.410.8139.4433.59Wildflower5717.260.89287.06
2478946.521.5523.013.350.8447.8730.24Avocado8074.581.00976.69
2478954.321.7720.786.410.8920.8222.39Wildflower3418.880.64204.93
2478961.991.6815.954.890.8742.1428.95Tupelo1783.780.80744.25
2478971.341.2212.723.770.7223.2933.15Lavender5152.580.82496.27
2478981.981.2917.904.820.8936.1034.69Rosemary8261.631.00754.98
2478996.181.6719.544.910.8531.1520.82Acacia6939.391.00543.41
2479007.781.4915.785.690.7344.6044.07Chestnut4139.790.64615.46
2479015.781.7414.966.810.8347.1937.79Avocado4417.740.97949.32
2479028.961.8618.626.890.8625.9442.88Lavender8119.620.64384.48